Our method is illustrated in Fig. REF . To ensure demographic parity, it extracts a representation of \(x\)  that is independent of \(a\)  but captures all other information about \(x\) .
To do so, we adopt the class-based disentanglement framework of [1]}.
Specifically, we consider two feature extractors, \(F_a\)  and \(F_z\) , where \(F_a\)  encodes only the information relevant for the classification of \(a\) , and \(F_z\)  contains all other information in \(x\) . To this end, we train \(F_a\)  and \(F_z\)  in two steps.
